d_UMAP: Uniform Manifold Approximation and Projection (UMAP)

In egenn/rtemis: Machine Learning and Visualization

Uniform Manifold Approximation and Projection (UMAP)

Description

Perform UMAP decomposition using uwot::umap

Usage

d_UMAP(
  x,
  x.test = NULL,
  k = 2,
  n.neighbors = 15,
  init = "spectral",
  metric = c("euclidean", "cosine", "manhattan", "hamming", "categorical"),
  epochs = NULL,
  learning.rate = 1,
  scale = TRUE,
  verbose = TRUE,
  ...
)

Arguments

x	Input matrix
x.test	Optional test set matrix. Will be projected on to UMAP bases
k	Integer: Number of projections
n.neighbors	Integer: Number of keighbors
init	Character: Initialization type. See ⁠uwot::umap "init"⁠
metric	Character: Distance metric to use: "euclidean", "cosine", "manhattan", "hamming", "categorical". Default = "euclidean"
epochs	Integer: Number of epochs
learning.rate	Float: Learning rate. Default = 1
scale	Logical: If TRUE, scale input data before doing UMAP. Default = TRUE
verbose	Logical: If TRUE, print messages to screen. Default = TRUE
...	Additional parameters to be passed to uwot::umap

Details

Updated 2023-12-09: See GitHub issue and related comment

Value

rtDecom object

Author(s)

E.D. Gennatas

See Also

Other Decomposition: d_H2OAE(), d_H2OGLRM(), d_ICA(), d_Isomap(), d_KPCA(), d_LLE(), d_MDS(), d_NMF(), d_PCA(), d_SPCA(), d_SVD(), d_TSNE()

egenn/rtemis documentation built on May 4, 2024, 7:40 p.m.